Assessing Mental Workload from Skin Conductance and Pupillometry using Wavelets and Genetic Programming

Abstract

An essential component of augmented cognition (AC) is developing robust methods of extracting reliable and meaningful information from physiological measures in real-time. To evaluate the potential of skin conductance (SC) and pupil diameter (PD) measures, we utilized a dual-axis pursuit tracking task where the control mappings repeatedly and abruptly rotated 90° throughout the trials to provide an immediate and obvious challenge to proper system control. Using these data, a model-building technique novel to these measures, genetic programming (GP) with scaled symbolic regression and Age Layered Populations (ALPS), was compared to traditional linear discriminant analysis (LDA) for predicting tracking error and control-mapping state. When compared with traditional linear modeling approaches, symbolic regression better predicted both tracking error and control mapping state. Furthermore, the estimates obtained from symbolic regression were less noisy and more robust.